Simulating and mapping spatial complexity using multi-scale techniques

Abstract. In the last few years, the 3D GIS domain has developed rapidly, and has become increasingly accessible to different disciplines. 3D Spatial analysis of Built-up areas seems to be one of the most challenging topics in the communities currently dealing with spatial data. One of the most basic problems in spatial analysis is related to visibility computation in such an environment. Visibility calculation methods aim to identify the parts visible from a single point, or multiple points, of objects in the environment. In this work, we present a unique method combining visibility analysis in 3D environments with dynamic motion planning algorithm, named Visibility Velocity Obstacles (VVO) with Markov process defined as spatial visibility analysis for routes in 3D dense city environment. Based on our VVO analysis, we use Reinforcement Learning (RL) method in order to find an optimal action policy in dense 3D city environment described as Markov decision process, navigating in the most visible routes. As far as we know, we present for the first time a Reinforcement Learning (RL) solution to the visibility analysis in 3D dense environment problem, generating a sequence of viewpoints that allows an optimal visibility in different routes in urban city. Our analysis is based on fast and unique solution for visibility boundaries, formulating the problem with RL methods.

The origins of modern spatial analysis lie in the development of quantitative geography and regional science in the late 1950s. The use of quantitative procedures and techniques to analyse patterns of points, lines, areas and surfaces depicted on analogue maps or defined by co-ordinates in two-or three-dimensional space characterise the initial stage. Later on, more emphasis was placed on the indigenous features of geographical space, on spatial choices and processes, and their implications for the spatio-temporal evolution of complex spatial systems. Spatial analysis, as it has become over the past four decades, is more than spatial statistics and data analysis, and goes far beyond data sampling, data manipulation, exploratory and confirmatory spatial data analysis, into areas of spatial modelling encompassing a large and diverse set of models in both the environmental and the social sciences. In the environmental sciences, they range from physical dispersion models (e.g. for suspended particulates), chemical reaction models (e.g. photochemical smog), and biological systems models (e.g. for ecosystems in water), through deterministic process and stochastic process models of regional environmental quality management, and integrated models for environment-energy-economic assessment. For many models, especially the physical dispersion models, chemical reaction models, and biological systems models, the geographical location of the site is not considered to be of overriding importance. The variation in model results over space can be achieved by obtaining different inputs to the area in question. Process models include deterministic versions which attempt to describe a particular sequence in terms of known physical laws, and stochastic model versions which aim to describe a particular process such as erosion, groundwater movement and absorption of pollutants in terms of probability theory. For such processes, there is an interaction between the spatial process and a substrate. This substrate provides a one-, two-or three-dimensional framework within which the process model can operate. The space in which the process is modelled is disaggregated into a set of finite elements which are usually assumed to be internally homogeneous. Two-dimensional models generally use as their data small pixels (see Fischer et al., 1996).

Outlier detection is an important problem in spatial analysis which involves finding a region of spatial locations with features significantly different from the rest of the population. In this paper, we used fast fourier transform to highlight the areas with high frequency change. The spatial points identified by the fourier transform are then reconfirmed with Z-value test and outlier regions are identified. We performed several experiments to highlight the accuracy and efficiency of the approach and compared it with some other existing approaches.

In searching for a track record of earth formation, people tend to interpret field data in a way that limits the potential of history and explanation, which is often less influenced by theory and available facts, this requires valid data gathered intellectually. Geocomputing, which is the digital processing of geographic data, is a relatively recent field that reflects a wide range of research methodologies and their application to help solve problems in machine learning, geostatistics, image processing, and spatial analysis, among other areas. The purpose of this study was to assess the effectiveness of using geocomputational methods with Avenza Map and ArcGIS. This study uses geocomputation and spatial analysis to carry out geological mapping at a scale of 1:25,000 located in the Battang area, West Wara District, Palopo City, South Sulawesi. Two aspects are the main focus in implementing this research method, namely when mapping by relying on Avenza with a base map sourced from the Rupa Bumi Indonesia (RBI) map and on data management using the ArcGIS application to become a 3D map. Lithological of the study area showed basalt, porphyry basalt, and phyllite. The geological structure that develops in the research area is joint and fault. Avenza Map work with spatial analysis in ArcGIS is obtained cognitively, involving the relationship between individual conditions, tools, and terrain conditions. It can be stated that the use of avenza on smartphones in this modernization condition is more effective. This research allows for a review of the practical level of use of computer methods ranging from mapping to data management. Regarding implementing cognitive mapping, Avenza Map and ArcGIS can be recommended tools in geo-computation techniques as more effective support for recording and managing data while still paying attention to noise that may occur as a barrier to data accuracy.

Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matern covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

This review first gives an overview on the concept of fractal geometry with definitions and explanations of the most fundamental properties of fractal structures and processes like self-similarity, power law scaling relationship, scale invariance, scaling range and fractal dimensions. Having laid down the grounds of the basics in terminology and mathematical formalism, the authors systematically introduce the concept and methods of monofractal time series analysis. They argue that fractal time series analysis cannot be done in a conscious, reliable manner without having a model capable of capturing the essential features of physiological signals with regard to their fractal analysis. They advocate the use of a simple, yet adequate, dichotomous model of fractional Gaussian noise (fGn) and fractional Brownian motion (fBm). They demonstrate the importance of incorporating a step of signal classification according to the fGn/fBm model prior to fractal analysis by showing that missing out on signal class can result in completely meaningless fractal estimates. Limitation and precision of various fractal tools are thoroughly described and discussed using results of numerical experiments on ideal monofractal signals. Steps of a reliable fractal analysis are explained. Finally, the main applications of fractal time series analysis in biomedical research are reviewed and critically evaluated.

GIS is a geo-referenced data based system for manipulating digital spatial and thematic data. Because of its unique properties of spatial data (spatial dependence and spatial heterogeneity), there is an urgent need to investigate the mechanisms of spatial processes through exploring spatial data. The techniques of spatial analysis can be categorized into six groups: (1) generation of spatial data and its preliminary treatment; (2) spatialization of attribute data and scale transformation of spatial data; (3) visualization and exploration of spatial data; (4) geo-statistics; (5) analysis of lattice data; and (6) reversing information from complicated spatial data. To meet the real world solutions, a procedural diagram of spatial data analysis is suggested as below: (1) preliminary data treatment; (2) modeling; (3) solution; and (4) interpretation. Various techniques are being employed to investigate spatial processes, the identical study objective and some common characteristics of the study object may lead to the integration and convergence of the various approaches, and further the introduction of a general science: spatial mathematics. A same important issue is to develop robust spatial analysis computer packages for public uses.

Evolutionary programming (EP) is an application of the concepts of Darwinian evolution to complex optimisation problems. This is primarily addressed in the literature through the use of genetic algorithms (GAs), but there are problems where a hybrid approach coupling the robustness of GAs with the effectiveness of a heuristic procedure may yield better results. This paper focusses on the development and use of such a hybrid EP algorithm to solve a particular multi-objective spatial object-location problem. The domain knowledge which forms part of the heuristics of the methodology developed is provided by the problem of citing sustainable water management strategies within the urban fabric, taking into account social, economic, technical and cost parameters and constraints.

The need to transfer information across a range of space–time scales (i.e. scaling) is coupled with the need to predict variables and processes of interest across landscapes (i.e. distributed simulation). Agricultural landscapes offer a unique set of problems and space–time data availability with the onset of satellite‐based positioning and crop yield monitoring. The present study addresses quantification of the spatial variability of rainfed crop yield and near‐surface soil water at farm field scales using two general methods: (1) geostatistical and fractal analyses; and (2) univariate linear regression using topographic attributes as explanatory variables. These methods are applied to 2 years of crop yield data from three fields in eastern Colorado, USA, and to soil‐water content (depth‐averaged over the top 30 cm) in one of these fields. Method 1 is useful for scaling each variable, and variogram shapes and their associated fractal dimensions of crop yield are related to those of topographic attributes. A new measure of fractal anisotropy is introduced and estimated from field data. Method 2 takes advantage of empirical and process knowledge of topographic controls on water movement and microenvironments. Topographic attributes, estimated from a digital elevation model at some scale (10 m by 10 m spacing here), help explain the spatial variability in crop yield. The topographic wetness index, for example, explained from 38 to 48% of the spatial variance in 1997 wheat yield. Soil water displays more random spatial variability, and its dynamic nature makes it difficult to predict in both space and time. Despite such variability, spatial structure is evident and can be approximated by simple fractals out to lag distances of about 450 m. In summary, these data and spatial analyses provide a basis and motivation for estimating the fractal behaviour, spatial statistics, and distributed patterns of crop yield from landscape topographic information. Copyright © 2004 John Wiley & Sons, Ltd.

This article will be positioned on our previous work demonstrating the importance of adhering to a carefully selected set of criteria when choosing the suitable method from those available ensuring its adequate performance when applied to real temporal signals, such as fMRI BOLD, to evaluate one important facet of their behavior, fractality. Earlier, we have reviewed on a range of monofractal tools and evaluated their performance. Given the advance in the fractal field, in this article we will discuss the most widely used implementations of multifractal analyses, too. Our recommended flowchart for the fractal characterization of spontaneous, low frequency fluctuations in fMRI BOLD will be used as the framework for this article to make certain that it will provide a hands-on experience for the reader in handling the perplexed issues of fractal analysis. The reason why this particular signal modality and its fractal analysis has been chosen was due to its high impact on today’s neuroscience given it had powerfully emerged as a new way of interpreting the complex functioning of the brain (see “intrinsic activity”). The reader will first be presented with the basic concepts of mono and multifractal time series analyses, followed by some of the most relevant implementations, characterization by numerical approaches. The notion of the dichotomy of fractional Gaussian noise and fractional Brownian motion signal classes and their impact on fractal time series analyses will be thoroughly discussed as the central theme of our application strategy. Sources of pitfalls and way how to avoid them will be identified followed by a demonstration on fractal studies of fMRI BOLD taken from the literature and that of our own in an attempt to consolidate the best practice in fractal analysis of empirical fMRI BOLD signals mapped throughout the brain as an exemplary case of potentially wide interest.

Landslide susceptibility map delineates the potential zones for landslides occurrence. The paper presents a statistical approach through spatial data analysis in GIS for landslide susceptibility mapping in parts of Sikkim Himalaya. Six important causative factors for landslide occurrences were selected and corresponding thematic data layers were prepared in GIS. Topographic maps, satellite image, field data and published maps constitute the input data for thematic layer preparation. Numerical weights for different categories of these factors were determined based on a statistical approach and the weighted thematic layers were integrated in GIS environment to generate the landslide susceptibility map of the area. The landslide susceptibility map classifies the area into five different landslide susceptible zones i.e., very high, high, moderate, low and very low. This map was validated using the existing landslide distribution in the area.

Transgressive coastal dunefields dominate the southern coast of Brazil, and few studies are investigating the factors driving their historical changes. The aim of this study is to investigate spatio-temporal changes over the last 60 years in the Itapeva coastal transgressive dunefield (northern coast of Rio Grande do Sul state) and also the factors influencing these changes. The dunefield is analyzed via various methods including (i) spatial and temporal analyses of the dunefield utilizing aerial photography, satellite imagery and Geographic Information System, (ii) climatic analysis from 1961 to present and (iii) sand drift calculations. The results show a significant decrease in the mobile dunes area since 1953, an increase in vegetation cover, and an increase in urbanization area. While anthropogenic factors have influenced the dunefield (e.g. sediment removal and urbanization development), results of this study show that an increase in rainfall and decrease in average wind velocity are also responsible for the main spatial changes, vegetation colonization and stabilization processes in the Itapeva dunefield. The dunefield has also evolved into a new stage where deflation basin development and enlargement has encouraged vegetation colonization and expansion

Mangrove forests are highly productive and have large carbon sinks while also providing numerous goods and ecosystem services. However, effective management and conservation of the mangrove forests are often dependent on spatially explicit assessments of the resource. Given the remote and highly dispersed nature of mangroves, estimation of biomass and carbon in mangroves through routine field-based inventories represents a challenging task which is impractical for large-scale planning and assessment. Alternative approaches based on geospatial technologies are needed to support this estimation in large areas. However, spatial data processing and analysis approaches used in this estimation of mangrove biomass and carbon have not been adequately investigated. In this study, we present a spatially explicit analytical framework that integrate remotely sensed data and spatial analyses approaches to support the estimation of mangrove biomass and carbon stock and their spatial patterns in West Africa. Forest canopy height derived from SRTM and ICESat/GLAS data was used to estimate mangrove biomass and carbon in nine West African countries. We developed a geospatial software toolkit that implemented the proposed framework. The spatial analysis framework and software toolkit provide solid support for the estimation and relative comparisons of mangrove-related metrics. While the mean canopy height of mangroves in our study area is 10.2 m, the total biomass and carbon were estimated as 272.56 and 136.28 Tg. Nigeria has the highest total mangrove biomass and carbon in the nine countries, but Cameroon is the country with the largest mean biomass and carbon density. The resulting spatially explicit distributions of mangrove biomass and carbon hold great potential in guiding the strategic planning of large-scale field-based assessment of mangrove forests. This study demonstrates the utility of online geospatial data and spatial analysis as a feasible solution for estimating the distribution of mangrove biomass and carbon at larger or smaller scales.

Mound tombs, popular in the south Yangzi River area in Shang and Zhou Dynasties, are regional cultural remains in China. With the aims of protecting and scientifically analysing cultural relics, laser scanning technology was adopted to study Zhaihuatou mound tombs located in Nonglin Village in Tianwang town, Jiangsu Province. Multiple tombs are held within one mound in good keep and with the typical construction of centripetalism. Accurate tomb LiDAR (Light Detection And Ranging) data were acquired by applying terrestrial laser scanning technology to the field mound excavation. Subsequently, a spatial morphological analysis of the tombs was conducted on the basis of archaeological rules and GIS spatial data processing methods. Using the theory of centripetalism construction of multi-tomb-one-mounds, we proposed an algorithm to determine the concentrated area of the geometric directions of tombs, and centripetalism theory was scientifically validated in comparing results with the excavation data. Spatial data clustering methods were used to analyse and deduce the spatial distribution characteristics of the tombs. We propose and demonstrate that the burial system is in the form of family-clan aggregation, and is useful for developing research on regional burials. Experimental data show that the proposed method is a novel example of how spatial analysis can foster more precise field archaeological excavations on a large scale, and it is significant to study various types of tombs, relics and ruins.

General-purpose optimization methods for parallelization of digital terrain analysis based on cellular automata